text:
Covariance of maximum likelihood evolutionary distances between sequences aligned pairwise.

BACKGROUND
The estimation of a distance between two biological sequences is a fundamental process in molecular evolution. It is usually performed by maximum likelihood (ML) on characters aligned either pairwise or jointly in a multiple sequence alignment (MSA). Estimators for the covariance of pairs from an MSA are known, but we are not aware of any solution for cases of pairs aligned independently. In large-scale analyses, it may be too costly to compute MSAs every time distances must be compared, and therefore a covariance estimator for distances estimated from pairs aligned independently is desirable. Knowledge of covariances improves any process that compares or combines distances, such as in generalized least-squares phylogenetic tree building, orthology inference, or lateral gene transfer detection.

RESULTS
In this paper, we introduce an estimator for the covariance of distances from sequences aligned pairwise. Its performance is analyzed through extensive Monte Carlo simulations, and compared to the well-known variance estimator of ML distances. Our covariance estimator can be used together with the ML variance estimator to form covariance matrices.

CONCLUSION
The estimator performs similarly to the ML variance estimator. In particular, it shows no sign of bias when sequence divergence is below 150 PAM units (i.e. above ~29% expected sequence identity). Above that distance, the covariances tend to be underestimated, but then ML variances are also underestimated.

Background
The estimation of evolutionary distances between gene/ protein sequences is one of the most important problems in molecular evolution. In particular, it lies at the heart of most phylogenetic tree construction methods. The estimation of such distances is a two step process: first, homologous characters are identified, then the distances are estimated from the character substitution patterns. The most accurate matching of homologous characters is obtained by multiple sequence alignments (MSAs). Indeed, by considering all sequences simultaneously, MSAs yield a consistent and in principle optimal grouping of the homologous characters. Unfortunately, MSAs are hard to compute optimally (time complexity exponential in the number of sequences), and thus are in practice computed using heuristics. Alternatively, the sequences can be analyzed exclusively on the basis of pairs of sequences, using an algorithm such as Smith-Waterman [1] that yields optimal pairwise alignments (OPAs). This approach is often taken by large-scale comparative genomics analysis such as MIPS, OMA or RoundUp [2][3][4], which analyze the sequences pairwise due to computational constraints.
Once the homologous characters are identified, the second step of distance estimation can proceed. The method of choice is a maximum likelihood (ML) estimation based on some model of evolution. There too, the distances can either be estimated simultaneously from all sequences using a combination of tree topology inference and joint optimization of all branches, or pairwise, by estimating the distances between every pair of sequences. Joint estimation requires MSAs, while pairwise distance estimation can be done from either OPAs or from the pairwise alignments induced by an MSA (IPAs). Fig. 1 provides an overview of the different approaches.
In all cases, the estimation of evolutionary distances is subject to inference uncertainty, which is commonly quantified by their variances and covariances. Indeed, the distance variance information can be used to build confidence intervals around the estimate; covariances of pairs of distances can be used to build the confidence intervals of combinations of distances. Examples of applications include generalized least squares (GLS) phylogenetic tree building [5] construction of confidence sets of trees [6], test for monophyly using likelihood ratios [7], comparison of evolutionary distances for orthology inference [3], or distance-based lateral gene transfer detection [8] Variance estimates are provided by ML theory in both joint and pairwise distances estimation. However, ML theory only provides covariance estimates if all distances are estimated jointly. Covariance estimates for distances computed from IPAs in the context of specific parametric substitution models have been reported by Hasegawa et al. [9] and Bulmer [6], and were generalized by Susko [10] to all Markovian models of evolution. Furthermore, the covariance of distances from IPAs can also be estimated (though much more slowly) through bootstrapping [11]. As for the covariance of distances obtained from OPAs, the main difficulty in computing them is that, since sequence pairs are aligned individually, they usually have Overview of approaches to estimate evolutionary distances and their covariances Figure 1 Overview of approaches to estimate evolutionary distances and their covariances. A set of n sequences can be aligned jointly to obtain an MSA or in a pairwise optimal manner resulting in optimal pairwise alignments (OPAs). Given a hypothesis of character homology, distance estimation per ML can essentially be done in two ways: jointly on a tree or pairwise. In the first case a tree's branch-lengths are estimated simultaneously. This requires an MSA. In the second case pairwise distances are estimated either from MSA induced pairwise alignments (IPAs) or from the OPAs. The distance estimators are afflicted with an error expressed by their variances and covariances. In all cases, the covariances can be modeled as a function of shared branch lengths, but this requires a phylogenetic tree. When distances are estimated based on an MSA, the variances and covariances can be obtained from ML theory or by bootstrapping over the MSA's columns. In the case of OPAs, these techniques cannot be directly applied (see Methods). We have previously presented a covariance estimator for the case where the two OPAs in question share a sequence (i.e. for triplets). In this paper, we introduce an estimator for the general case. We have shown in a previous article [12] a numerical approximation for the constrained case of the covariance of two OPA distances involving a common sequence (i.e. on a triplet of sequences), for empirical substitution models such as PAM or JTT. In this article, we present an estimator for the covariance of ML distances estimated from OPAs that works on triplets and quartets of sequences. This solves the problem of sets of sequences of arbitrary size, because each covariance involves at most four sequences at a time. Thus, the full covariance matrix is naturally obtained through quartet analysis. We analyze the performances of the estimator in terms of bias and variance. Finally, we compare the results obtained on triplets of sequences to our previous work.

Results and Discussion
In the following, we present and analyze the performances of the estimator for the covariance of two distances. For this purpose, it is informative to analyze the results separately for the following three different underlying topological relations, illustrated in Fig. 2:

Case of dependence
The two distances are estimated between four distinct sequences, and they have some evolution in common (i.e. the two distance involve a common branch on the tree).
With such an evolutionary history, the two distances estimates covary positively.

Case of independence
The two distances are estimated between four distinct sequences, but they have no evolution in common (i.e. the two distance involve distinct branches on the tree). This case is informative, because a central assumption in most evolutionary models is that evolution on different branches is independent [13]. With no branch in common, the distances should not covary [6]. Thus, such a topology can be used to test the estimators as negative control.

Case of triplet
The two distances involve a common sequence, and have some evolution in common. This case is of special interest, because we have previously presented an alternate estimator for this particular case using a different approach [12]. Thus, we can compare our results to this approach, hereafter called "the numerical approximation".
Note that the covariances are estimated using the same algorithm in all three cases: we only distinguish them from each another for the purpose of this analysis.
To assess the performance of the covariance estimator, it was compared against the Monte Carlo covariance estimator. In short, each point shown in the figures was obtained from 40,000 sets of sequences mutated along a random quartet subtree of the tree of life (see Methods below). That way, the evaluation is based on tree samples that are distributed as closely as possible to real biological data. To account for gene families with varying rates, each quartet was scaled with a random factor uniformly distributed between 0.5 and 2. Note that results corresponding to very large distance constitute extreme cases; for instance, when sequences are 150 PAM units apart, each position has, on average, mutated 1.5 times. Fig. 3a shows the mean of our estimator versus the Monte Carlo estimator in nine scatterplots arising from combining the topologies mentioned above (rows) with three different sequence lengths (columns). In the case of dependence, the first row, we see that our estimator lies in about 80% of the cases within the 95% confidence interval of the Monte Carlo estimator. In the case of independence, both estimators are close to zero, though our estimator shows a minor upward bias in some cases. The third row gives the result of both the covariance estimator introduced here, as well as the numerical approximation from our previous study [12]. Here, we see that though the former performs well in cases of lower covariance values, it shows a clear downward bias in cases of larger covari-  ances. The numerical approximation does not present any apparent sign of bias, which is hardly surprising, given that it was obtained through regression. What is however surprising, is that, given its simple structure, it performs better than the covariance estimator, which takes into account more data and is backed by a more detailed model.

Possible topological relations of sequences
It is instructive to compare the absolute bias of the covariance estimator to the well-known ML variance estimator Comparison of the covariance estimator and the ML variance estimator with their Monte Carlo counterparts (see e.g. [14]). As can be seen in Fig. 3b, the ML variance is also biased for high variance values. We conjecture that this is mainly due to mis-aligned positions, which cause model violations in the parameter estimation. This problem is also likely to affect the covariance estimator. Even more directly, the ML variance estimator is a factor in the expression of the covariance estimator (see Methods), so any error in the ML variance is propagated to the covariance estimator. At this point, improving the ML estimator for cases of high divergence is likely to require better alignments, or an explicit modeling of the mis-aligned positions, which is beyond the scope of the present work.
Further, to put the bias of the covariance estimator into perspective, we compared it to the standard deviation of the estimator. Fig. 4 presents the bias and standard deviation as function of the average number of anchors for sequence length of 500. The anchors are the positions that are consistently aligned in the OPAs involved (see methods for the precise definition). Both bias and standard deviation strongly depend on the fraction of anchors, which can be thought of as a measure of alignment quality. Fig. 5 depicts the dependency between percentage of anchors and average distance. As one would expect, the fraction of anchors decreases as divergence increases. For a fraction of anchor positions below 60%, the average of the two distances involved in the covariance computation is always greater than 150 PAM. In Fig. 4, we first consider the bias and standard deviation for the case of dependence. When the fraction of anchor positions is above 60% (this is the case for approximately 85% of the quartets of sequences in families of orthologs in OMA [3], data not shown), the bias is far smaller than the standard deviation, and is therefore likely to have little negative impact in practice.
In the case of triplets, the bias exceeds the standard deviation already when the fraction of anchors is about 80%.
The ML variance estimator has this transition around 75% of anchors. In the case of independence, where we expect our covariance estimator to be zero, its bias is always much smaller than its standard deviation (data not shown).
Most applications of the covariance estimator involve the covariance matrix. Let be an approximation to the matrix A. We refer to as the relative error in , where ||·|| 2 denotes the two-norm. Fig. 6 shows the relative error of the 2 × 2 variance-covariance matrices computed with the ML variance estimator in the diagonal entries and our covariance estimator in the off-diagonal entries, and the same 2 × 2 matrices with only diagonal entries. The plots show that for the dependence case the the matrices with both covariance and ML variance estimators have a equal or lower relative error than the matrices with the ML variance only, except for a few cases in the region with a high fraction of anchors. In the triplet case, the variance-covariance matrices have always lower error then variance matrices. Finally, in the case of independence, the matrices with covariance do not always have lower relative error, but this is expected, because the true covariance is null in this special case.

Conclusion
We have presented a method to estimate the covariances of distances estimated from pairwise alignments. It does not require the construction of MSAs, which are hard to compute and therefore are only approximated in practice. Furthermore, it does not rely on phylogenetic trees as it is the case with covariance estimation from joint ML, or in covariance estimation methods that model the covariances as a function of shared branch lengths [15,16]. Treê Bias and standard deviation of the covariance and ML variance estimators Figure 4 Bias and standard deviation of the covariance and ML variance estimators. Average percentage of anchors vs. bias and standard deviation of the covariance estimator for sequence length of 500 AA. Error-bars indicate the 95% confidence intervals. The bias increases with decreasing fraction of anchors. The bias is smaller than the standard deviation when percentage of anchors is greater than 65% (dependence), 80% (triplet) and 75% (ML variance).
building is not only a costly process, but is also subject to inference errors.
The accuracy of our estimator is comparable to the ML variance estimator. Both estimators are biased but in both cases the bias is, for distances below 150 PAM, far smaller than their standard deviation. The bias of the covariance estimator (as well as the ML variance, and to some extent the distance estimators) becomes worse with declining percentage of anchors. These biases arise because the alignment positions under scrutiny do not constitute an unbiased subsample of the true homologous positions. Note that misaligned positions are likely to affect distances from MSAs too. A solution to this problem would lead to better distance estimates in the first place. In the meanwhile, it is probably best to issue a warning if the percentage of anchors falls below some threshold.
The estimation of evolutionary distances is a very important process in molecular evolution, and therefore the covariance estimator presented here will be of use for various applications, such as the construction of GLS trees on OPA distances, the construction of confidence sets of trees based on the GLS test statistic, relative-rate tests, distancebased lateral gene transfer detection, and in general in any process that needs to estimate confidence of distance combinations.

Covariance of distances from OPAs
In this section we derive a covariance estimator for ML distances from OPAs.

Preliminaries
The columns of an MSA are a consistent hypothesis of character homology for a set of sequences. With OPAs on the other hand, we have the problem that for a set sequences, the resulting pairwise alignments are not always consistent in their inference of the homologous characters. Fig. 7 depicts an example. Let s i,j be the character at position j in a sequence s i . Only characters in bold, for example {s 1,1 , s 2,1 , s 3,1 , s 4,1 }, are consistently aligned in the OPAs. We call such a consistent set of characters an anchor. On the other hand, s 1,2 is aligned to s 2,2 and to s 3,2 , so in a consistent situation it would follow that s 2,2 and s 3,2 should be aligned, but it is not the case. Relative error of covariance matrix Figure 6 Relative error of covariance matrix. Average relative error of variance matrices and variance/covariance matrices for a sequence length of 500 AA. Dependence and independence cases: Variance matrices and variance-covariance matrices have comparable error. Triplet case: Variance-covariance matrices have lower error. Formulation of the covariance estimator Let p(X j , d) denote the probability of a homologous character-pair X j for the j-th OPA when the distance is taken to be d. We assume that the gap-positions have been removed from the alignments and that the j-th OPA has length n j . Denote the distance obtained by ML and d j the true distance. It is well known from ML theory (see e.g. [14]) that under appropriate smoothness conditions, the variance of is Let the score function for the j-th OPA be where x j,l is the realization of X j at position l. To abbreviate, we set . As mentioned by Susko [10], ML results yield Based on equation (3) we derive now an expression for the covariance of two distance estimates and .
Along this paper, variables with a superscript A refer to anchors, N refer to non-anchors. Since virtually all Markovian models of evolution assume independent positions, we can split the score functions in a part corresponding to the anchor positions and a non-anchor part: We assume that the sums in and are ordered such that and are part of the same anchor  Correlations between distance arise from common mutation events (on common branches on the "true" tree). As mentioned above, positions in a sequence are stochastically independent from one another. We assume that the anchors are correctly aligned. Consequently, characters in the anchor and non-anchor parts cannot be homologous to each other. Therefore and are both zero. The expression becomes where n A is the number of anchors. Because of the correctness assumption of the anchors, all pairs that are not part of the same anchor are non-homologous, and therefore, their covariance is zero, i.e. for l ≠ m, and we get We assumes that the are i.i.d. We denote the corresponding random variables . The assumption is jus-tified due to the Markov model and the correctness assumption of the anchors. As to the some proportion may be homologous, but we do not know which one. Determining the homologous pairs would solve the problem of MSA construction (known to be hard and not our goal here). Instead, we take the working assumption that the and do not covary. With the two assumptions the expression of the covariance approximation becomes: By using the form of equation (12), we obtain an estimator for the covariance. The variance V j is estimated by The estimate for the covariance of the anchor part is the well-known unbiased estimator where denotes the sample mean.

Simulation methods
To evaluate the performance of the covariance estimator we performed a Monte Carlo simulation on quartets and compared our estimator to the sample covariance (also referred to as the Monte Carlo covariance).

Sampling of quartets
The quartets were sampled uniformly from a variance weighted least squares (WLS) tree on 352 species. The WLS tree was inferred by the LeastSquaresTree function in Darwin [17]. To obtain the input distance and variance matrices for LeastSquaresTree we used data from the OMA project [3]. The inter-species distances were determined as average PAM distances over sets of groups of orthologs. A total of 100 quartets were sampled, each one contributing one data-point to the plots shown here.
Simulation procedure for one quartet To explore the branch-length space, while preserving the relative branch-length structure given by the WLS tree we applied an uniformly distributed U(0.5,2) expansion/ contraction factor on each quartet. Then, we generated 40,000 times three random sequences of length m = {200, u j A 500, 800} and mutated each of them along the dilated model quartet. We assumed a Markovian model of evolution using the updated PAM matrices [18] and introduced gaps of Zipfian distributed length [19].
We applied our covariance estimator on each of the 40,000 quartets and estimated its expected value and variance to compare it against the sample covariance which we also refer to as Monte Carlo covariance. In the analysis of the results, we treated the sample covariance as a reference value, as it constitutes an unbiased estimator for the true covariance. The biases reported in the result section are defined as the estimate of the expected value of our covariance estimator minus the Monte Carlo covariance. Note that being an estimator itself, the sample covariance's variance had also to be taken into account in the analysis of the results.

Authors' contributions
CD and MG equally contributed to ideas, execution and writing.


extfieldsofstudy:
Materials Science
Medicine

text:
Potential clinical applications of quantum dots

The use of luminescent colloidal quantum dots in biological investigations has increased dramatically over the past several years due to their unique size-dependent optical properties and recent advances in biofunctionalization. In this review, we describe the methods for generating high-quality nanocrystals and report on current and potential uses of these versatile materials. Numerous examples are provided in several key areas including cell labeling, biosensing, in vivo imaging, bimodal magnetic-luminescent imaging, and diagnostics. We also explore toxicity issues surrounding these materials and speculate about the future uses of quantum dots in a clinical setting.

Introduction
Colloidal quantum dots (QDs) have completed the transition from a once curious demonstration of quantum confi nement in semiconductors to ubiquitous fl uorophores providing unique insights in biological investigations (Alivisatos 2004;Medintz et al 2005b;Michalet et al 2005). With well-established inorganic synthetic techniques now available for generating high quality QDs in solution, many experimentalists have focused on applications using these novel materials that exploit the numerous unique physical and optical properties of these luminescent nanoparticles. In 1998, two seminal papers appearing consecutively in an issue of Science provided the fi rst glimpse of the vast potential of quantum dots as probes for studying biology as well as a strong impetus for their rapid growth in such applications (Bruchez et al 1998;Chan and Nie 1998). As we approach a decade since those initial reports, the current literature is rife with examples of quantum dots used in specifi c biological applications. This trend is directly attributable to the unique characteristics exhibited by these nanocrystals and related to considerable improvements in synthetic techniques. Although they are often portrayed simply as alternatives to organic dyes, QDs have many important distinguishing characteristics that are unlike those of molecular fl uorophores (Murray et al 2000). In particular, resistance to photobleaching is unparalleled in semiconductor nanoparticles allowing them to retain their luminescent properties for remarkably long periods under continuous illumination. Perhaps most widely known is the variation of QD optical properties with particle size (Figure 1a) which permits tunable absorption and emission spectra. QDs can also be synthesized to be effectively monodispersed, leading to exceptionally narrow emission spectra with desirable symmetric profi les (cf. Figure 1b). While these principal advantages of colloidal QDs have been known for many years, only recently has the fi eld matured to the extent that there is signifi cant practical use of these materials.
Biological applications require luminescent probes that remain stably dispersed in aqueous media over a wide range of pH and ionic strengths, and accordingly there has been substantial effort devoted to generating water-soluble nanocrystals. Early studies considered aqueous phase synthesis using inverse micelles which naturally produce Potential clinical applications of quantum dots water-soluble QDs, however these methods have generally produced lower quality materials than methods using organic coordinating solvents (Kortan et al 1990). Fortunately, there are now numerous effective methods available for creating hydrophilic QDs post-synthesis which can be divided into two main categories: complete cap exchange or native surface modifi cation. The former involves the displacement of hydrophobic ligands with hydrophilic moieties that coordinate with surface atoms in the outermost shell layer. In the case of CdSe-ZnS QDs, this usually entails replacing alkyl amines, phosphines, and other surface active ligands with thiols covalently linked to hydrophilic groups ( Figure 2). Mono and dithiol groups have been shown to stably bind QDs with ZnS shells (Chan and Nie 1998;Mattoussi et al 2000).
In select cases, these modifi ed materials are stable in aqueous media for months to a year (or more) without signifi cant aggregation or loss of function (Uyeda et al 2005). The latter method retains the native hydrophobic capping ligands, however an amphiphilic molecule (such as a phospholipid) is introduced that favorably associates with the hydrophobic alkyl chains while simultaneously exposing a hydrophilic group to the aqueous solvent (Dubertret et al 2002). This has the advantage of retaining the exceptional passivating features of the native hydrophobic ligands (eg, alkyl phosphines) and has to this point been the preferred procedure for commercially-produced biocompatible QDs. QD bioconjugates merge the specifi c biological function of molecules such as DNA, proteins, and peptides with the novel photophysical properties of luminescent inorganic nanocrystals ( Figure 1d). These hybrid nanomaterials are possible largely due to the solubilization methods described previously which allow biomolecules to remain stable and functional following assembly to nanoparticles. Traditional fl uorescence methods (using organic dyes or rare earth metal chelates) have revolutionized many clinical methods and procedures such as diagnostic assays. Similarly, QDs have considerable potential to further improve existing methods and inspire entirely new diagnostic strategies due to their extraordinary photophysical properties. This review discusses some of the present clinical applications of luminescent quantum dots and explores their future prospects in this area.

Cell labeling
One of the broadest uses of fl uorescent probes in biology is the labeling of cellular structures. Naturally, the earliest demonstrated uses of QDs in biology were to label cells with a new class of bright and stable fl uorophores. Multicolor labeling of cells is a powerful technique for visualizing many of these structures simultaneously, such as cytoskeletal proteins or organelles, and to elucidate intracellular processes. Although cell labeling with organic dyes has been commonplace for decades, using multiple labels simultaneously remains a cumbersome procedure due to the narrow absorption profi les of most dyes. Effective multicolor labeling requires an assortment of fi lters to properly excite and collect fl uorescence from specifi c dye molecules. Moreover, if laser excitation is used, multiple sources are typically required to excite all of the dyes labeling the cell which can be expensive and requires a complex microscopy arrangement specifi c to the experiment. The continuous excitation of dyes inevitably results in signifi cant photobleaching that quenches the luminescence over short time scales (seconds to minutes). This severely limits the practical observation time for a sample, even with the addition of various anti-bleaching chemical agents (Lakowicz 1999).
By contrast, QDs are excellent fl uorescent probes for long-term multicolor cell labeling. Due to their exceptionally broad absorption profi les (Figure 1a), they can be effi ciently excited at any wavelength smaller than their initial band edge absorption. As the excitation photon energy increases (and wavelength decreases), the QD absorption likewise increases suggesting that extremely large effective Stokes shifts (spectral intervals between excitation and emission maxima) are possible (Leatherdale et al 2002). The effective brightness per probe particle is also superior with quantum dots as evidenced by their large molar absorption cross-sections which are a consequence of their nanometer size and composition. Perhaps the most impressive feature of QDs is their resistance to photobleaching which is quantitatively orders of magnitude superior to common organic dyes. Occasionally, QDs can even demonstrate photo-induced brightening where the luminescence quantum yield (QY) noticeably increases under continuous excitation possibly due to annealing of their surfaces. Multiplexed emission is also more spectrally isolated with QDs due to narrow and symmetric fl uorescence profi les (routinely Ͻ40 nm full-width at half-maximum) which reduces signal crosstalk and increases the number of labels than can be used simultaneously in a single system (Dabbousi et al 1997). This is especially useful in quantitative fl uorescence studies where spectral deconvolution is required to monitor individual signal channels (De Rosa et al 2001).
Beginning with the fi rst successful demonstrations of QD cell labeling by the Alivisatos and Nie groups, QDs have been widely used as luminescent cell markers that identify molecular structures. However, overcoming the challenge  (Mattoussi et al 2000), (e) peptides containing appropriate high affi nity residues (e.g, Cys -C and His -H) (Pinaud et al 2004), (f) trishydroxypropyl phosphine (THPP) (Kim and Bawendi 2003), (g) dihydrolipoic acid-polyethylene oxide (DHLA-PEG) (Uyeda et al 2005), (h) dendrons (Guo et al 2003). Many of these can be processed further to crosslink the ligands, add new functional groups, and/or attach biomolecules (QD/ligands not drawn to scale). of creating stable, water-soluble materials that could be delivered within cells was formidable. Two unique strategies were ultimately used to achieve a similar goal. Bruchez and colleagues (1998) used phalloidin to specifi cally target actin fi laments, followed by exposure to streptavidin and biotinylated QDs to form a sandwich structure. Electrostaticallycharged QDs bearing trimethoxysilylpropyl urea and acetate groups were shown to label the nucleus. Alternatively, Chan and Nie (1998) demonstrated transferrin-mediated endocytic uptake of QDs within HeLa cells which resulted in effi cient and preferential delivery of transferrin-labeled QDs versus unlabeled QDs. These early studies proved that QDs could perform similar to organic dye fl uorophores within cells and provided initial guidance regarding successful strategies for effi cient cell delivery.
Many examples have since followed where QDs have been used to label cellular structures both within and external to the cell membrane. Using conventional methods, QDs have been delivered inside cells via receptor-mediated pathways where specifi c ligands were attached to QDs to induce cellular uptake, as well as nonspecifi c endocytosis (ie, pinocytosis) where cells were incubated with a concentrated QD solution (Jaiswal et al 2003). Delivery of QDs via endocytosis has inherent benefi ts because it invokes native cellular mechanisms to transfer nanoparticles across the cell membrane. It is consequently thought to be the least disruptive delivery method as well as the most versatile. QDs capped with compact carboxylated ligands have shown reasonable rates of nonspecifi c endocytosis (Jaiswal et al 2003), however receptor-mediated uptake has demonstrated better effi ciency by specifi cally targeting receptors displayed on the cell surface (Chan and Nie 1998;Lidke et al 2004;Bharali et al 2005). As expected, ligands previously identifi ed to cue and facilitate receptormediated endocytosis continue to function effectively when attached to QD surfaces resulting in effi cient delivery of the modifi ed nanoparticle assembly within cells, but with the potential drawback of the cargoes remaining largely confi ned inside the endosomes (Delehanty et al 2006). If the intended destination of a QD bioconjugate is a specifi c organelle or general release into the cytosol, this perpetual confi nement presents a signifi cant obstacle.
Alternative delivery methods that introduce microscopic mechanical defects in the cell membrane are also options for inserting nanoparticle cargoes, but with an elevated risk of traumatizing the cell which may compromise long term viability and perturb normal function. Electroporation and microcapillary injection have been used to introduce nanoparticles into live cells with varying success (Dubertret et al 2002;Derfus et al 2004a;Uyeda et al 2005). In particular, microcapillary injection is suitable for introducing well-dispersed QD bioconjugates directly into the cytosol which could be desirable for certain sensing applications. However, the serial nature of this method is time-consuming and therefore considerably limits the number of cells that can be labeled in a practical period and subsequently isolated. Other methods including ballistic delivery and scrape loading are also possibilities, but have not been widely reported.
With the availability of commercial labeling kits, the prevalence of QDs in cell labeling applications has increased dramatically. Despite their popularity and success, commercial materials currently have somewhat limited potential due to the use of specifi c proprietary coatings and surface ligands to passivate and stabilize the nanoparticles. Similar to the challenges encountered in gene therapy applications, given the complexity of targeted delivery and function within cells it is likely that more fl exible and customized QD systems will be required for long-term intracellular imaging applications.

Biosensing and energy transfer
Due to their unique physical and optical properties, colloidal QDs have been used to develop new methods of biosensing. By attaching biomolecules to the QD surface, it is possible to generate complex bioconjugates that merge biological specifi city and function with the desirable optical characteristics of QDs. In many cases the nanometer size of QDs allows the nanocrystal to become a central structural component that can accommodate numerous copies of a particular biomolecule (eg, protein or DNA) or several different biomolecules simultaneously; as a result, these bioconjugates are sometimes referred to as "nanosensors." As robust fl uorophores, QDs are compatible with conventional biosensing techniques that implement fl uorescence to produce a large measurable signal. Fluorescence resonance energy transfer (FRET) in particular has been a popular method of signal transduction due to its sensitivity to molecular scale interactions (Lakowicz 1999;Clapp et al 2004;Medintz et al 2004). An early FRET-based technique was demonstrated by Patolsky and colleagues (2003) for the study of telomerization and DNA replication dynamics. Thiolated DNA was attached to the surface of water-soluble CdSe-ZnS QDs. In the presence of appropriate enzymes (telomerase or Klenow fragment) and dye-labeled nucleotides, the emission spectra showed a time dependent red-shift indicating effi cient energy transfer from QD to dye specifi cally due to telomerization or replication, respectively. The telomerization results were corroborated with AFM images showing growth of DNA extending from the QD surface. The same group later looked at QD-based FRET for detecting DNA hybridization and cleavage (Gill et al 2005). They noted that full recovery of the fl uorescence emission following the addition of enzyme was not possible due to nonspecifi c interactions between the dyes and nanoparticles. This could be a signifi cant limitation in this particular arrangement, however nonspecifi c interactions might be reduced through unique surface passivation methods. Medintz and colleagues (2003) used a related approach to develop a prototype FRET-based QD biosensor capable of detecting the nutrient sugar maltose in solution. Maltose binding protein (MBP), pre-bound to an analog sugar-dye complex, was conjugated to water-soluble QDs resulting in many MBP attached to each QD. In this initial state, fl uorescence from the QD was signifi cantly quenched by the nonemissive dye. As maltose is added to solution, the analog sugar-dye complex is progressively displaced resulting in a substantial increase in fl uorescence signal with increasing concentration. A reagentless implementation of a maltose biosensor was also demonstrated where the QD transfers energy to a Cy3 dye covalently attached near the binding pocket of MBP (Medintz et al 2005a). Sensing is achieved by monitoring the Cy3 fl uorescence as a function of maltose concentration where the quantum yield of the dye varies due to differences in solvent interaction with the dye between the bound and unbound states.
In another demonstration of FRET-based biosensing, Medintz and colleagues (2006) developed QD-peptide bioconjugates designed to measure rates of enzymatic digestion. Peptides were synthesized to have domains specifi c for QD attachment, structural rigidity, protease recognition, and dye labeling. In a manner similar to the maltose biosensor, the intact QD bioconjugates exhibit quenched fl uorescence due to the proximity of the dye labels. However, as an appropriate enzyme is added to solution, some of the peptides are cleaved allowing the dyes to diffuse away from the QD surface resulting in an increased fl uorescence signal. By altering the recognition sequence (consisting of 2-4 carefully chosen amino acid residues), the biosensor can be tuned to respond to a specifi c enzyme. More recently, Shi and colleagues (2006) demonstrated a QD FRET-based probe for enzymatic activity using compact dye-labeled tetra-peptide ligands. The probe was able to detect increased metalloproteinase activity in the extracellular matrix surrounding cancerous breast cells. A very recent communication by Gill and colleagues (2006) reports a multifunctional enzymatic probe for tyrosinase and thrombin. The former is observed following the oxidation of tyrosine near the surface of a QD, and the latter by specifi c scission of a linker peptide which interrupts energy transfer.
An analogous signal transduction method using charge to modulate QD fl uorescence was demonstrated by Sandros and colleagues (2005) to detect maltose in solution. Similar to the FRET-based maltose biosensor described above, this method utilizes MBP to specifically recognize the disaccharide. However, in this system a Ru complex transfers charge to the nearby QD thus quenching fl uorescence when the protein is in an unbound/open conformation. As MBP binds maltose in solution, the hinge-bending movement of the protein positions the Ru complex suffi ciently far away from the QD surface to disrupt charge transfer and enable fl uorescence emission from the exciton. In a slightly different confi guration, the same group developed a nanosensor selective for the fatty acid palmitate using QDs bound to intestinal fatty acid binding protein (Aryal and Benson 2006). In this arrangement, it is thought that a solvent occupancy effect, rather than a protein conformational change, is responsible for modulating the QD fl uorescence. In both systems where a Ru complex is conjugated to QDs via a protein, the sensing method appears to be general for many protein-ligand pairs where charge transfer is responsible for signal transduction; however, the exact mechanism has yet to be fully elucidated.
Collectively, the preceding methods demonstrate the versatility and functionality of QDs in fl uorescence biosensors. Energy transfer in these systems allows sensitive measurements of events such as binding, digestion, or conformational changes occurring on the molecular scale. Many of the benefi ts of QDs are unique and allow development of nanosensors that can retain their function over long periods and potentially in living tissue. Most of the reports in the literature are preliminary demonstrations, yet they offer a tantalizing preview of what is possible using QDs.

In vivo imaging
The ability to visualize native processes occurring in living organisms is invaluable for clinical diagnostic applications, yet remains elusive in practice due to conventional imaging limitations and the availability of suitable fl uorescence markers. Regarding the latter, many organic dyes have very short lifetimes (∼1 ns), are susceptible to photodegradation, and Potential clinical applications of quantum dots show inadequate fl uorescence brightness (Lakowicz 1999). Additionally, tissue autofl uorescence can exhibit similar spectroscopic characteristics making it diffi cult to resolve the desired signal from unwanted background. Due to their unique photophysical properties, QDs are promising fl uorophores for in vivo fl uorescence imaging and can overcome many of the usual limitations of dyes.
An early study by Akerman and colleagues (2002) examined targeted delivery of QDs to specifi c locations within live mice, however fl uorescence imaging was performed after the animal was sacrifi ced and the tissue sectioned. Noninvasive, real-time in vivo fl uorescence imaging requires exciting fl uorophores and detecting their emission through tissue which is invariably hindered by scattering and absorption of both the excitation and emission wavelengths. Although CdSe-ZnS QDs are by far the most common choice for high quantum yield nanocrystals, their visible fl uorescence (restricted within a range from 470 to 650 nm) is not wellsuited for imaging through tissue. Nonetheless, Gao and colleagues (2004) showed that CdSe-ZnS QDs could be used to image human tumors implanted in mice by implementing an image processing algorithm to isolate signal contributions from QDs and autofl uorescence. However, their method was limited to imaging structures very near the surface due to the limited penetration depth of visible wavelengths. A similar study was reported by Ballou and colleagues (2004) where they used several imaging methods, including transmission electron microscopy, to explore long term QD partitioning in tissue.
Using a different semiconductor core material and specifying an appropriate nanocrystal size, QDs can be excited and observed in the near-infrared (near-IR or NIR) which coincides with a range of optical transparency for living tissue (Figure 3a) (Lim et al 2003). Due to their size and composition, QDs exhibit an unusually large molar extinction coeffi cient which leads to improved absorption of photo-excitation and effective brightness. Size tuning can further optimize the peak emission wavelength for maximum fl uorescence penetration through relatively thick tissue (Ͼ1 cm). This concept was fi rst demonstrated by Kim and colleagues (2004) using so-called "Type II" QDs composed of core-shells with offset bandgaps. In this study, near-IR QDs were used to identify sentinel lymph nodes (directly invaded by metastatic cancer cells) in mice. Using a fi ltered halogen source and an IR camera, the collection of QDs within tissue was monitored in real-time to identify a region for surgical resection (Figure 4). The size of these nanocrystals (∼15-20 nm diameter) was shown to be ideal for their effi cient collection and retention in lymph nodes. This study demonstrates the feasibility of near-IR imaging for real-time surgical assistance, however the toxicity issues (to be discussed) associated with most QD materials remain a formidable hurdle to use in humans.
In vivo fl uorescence imaging has undoubtedly benefi ted from the development of QD fl uorophores where their unique properties allow more fl exible imaging strategies. For example, the expanded use of multi-photon microscopy can provide a more effi cient method of exciting QDs in vivo. This is due to the exceptional multi-photon absorption cross-section of semiconductor nanocrystals (Larson et al 2003). In certain cases, the effective brightness of QD fl uorophores is two or more orders of magnitude higher than organic dyes using multi-photon excitation. By preferentially exciting fl uorophores very near the highly focused excitation beam, high-resolution fluorescence images can be generated while eliminating out-of-focus and autofl uorescence contributions. This method has yet to be fully realized, however. Other in vivo imaging modalities such as magnetic resonance imaging (MRI) and positron emission tomography (PET) could also benefi t from QDs (Michalet et al 2005).

Diagnostics
One medical area where QDs may have signifi cant impact is in diagnostics and clinical assays. The unique properties of QDs have been investigated almost exclusively for two techniques that require the use of diagnostic fl uorophores: immunolabeling and nucleic acid detection. As QDs are a relatively new class of materials, the focus has been primarily on proof-of-concept demonstrations with various bioconjugation approaches being tested.

Immunolabeling
For the purposes of this review, we defi ne immunolabeling loosely as the use of antibody-driven specifi c binding to a target protein or biomolecule and the visualization of this event with some form of QD labeling. Table 1 presents a semi-comprehensive list where QDs and immunolabeling have been used for mostly proof-of-concept diagnostic purposes.
Many of the reported examples target the detection of various cancer markers within cells including c-Abl, CA-125, p-glycoprotein, mortalin, cytokeratin, prostate specifi c membrane antigen, and carcinoembryonic antigen (see Table 1). These markers are associated with transformation or metastasis and are usually indicative of both the cancer and its progression and are thus of pathological interest. Other uses include intracellular viral monitoring, blood cell antigen typing, visualizing drug therapy effects on cellular metabolism, and monitoring of cell markers. The only prominent nonimmunological exception is an example where the metastatic potential of cell lines was shown to correlate well with their ability to take up QDs from culture wells during migration (Parak et al 2002). However, this is more of a scientifi c curiosity and will likely never fi nd use as a prognostic indicator.
For almost half of the examples cited in Table 1, commercially available QDs (Quantum Dot Corporation/ Invitrogen) have been used which are either purchased pre-coated with streptavidin (for conjugation with biotinylated antibodies) or some type of species-specifi c IgG. This suggests that for many of these immunolabeling uses, commercial QD materials may be adequate and thus obviate the need to synthesize, solubilize, and functionalize custom materials. The greatest potential of QDs in this area is "multiplexing" or the simultaneous detection of multiple targets, since multiple colors of QDs can be excited with a single wavelength far removed from their respective emissions (Figure 3b). Indeed, examples have been reported of simultaneous cellular labeling with up to fi ve QD colors (Medintz et al 2005b). In terms of pathology and diagnosis, this would enable the colocalization of many markers within the same cells/tissues and allow determination of their spatial distribution. Based on the typical emission spectra of QDs, it is reasonable to anticipate ten or more colors of QDs (or QDs and dyes) could be used simultaneously. Furthermore, Potential clinical applications of quantum dots if a UV excitation source is used in combination with more red-emitting QDs (approaching the near-IR), brighter signals can be achieved as the molar extinction coeffi cients of these materials increases signifi cantly towards the UV. However, the limiting barrier is bioconjugation. Streptavidin-coated QDs will bind any biotinylated molecule indiscriminately and thus each "color" or antibody-labeled QD must either be prepared separately or a different and specifi c attachment scheme must be found for each combination of antibody and QD. For example the use of anti-mouse IgG coated QDs (targeting mouse-derived antibodies) in combination with anti-goat IgG coated QDs (targeting goat-derived antibodies) and streptavidin coated QDs, etc. This is not easily accomplished as there are currently a limited number of such QD coatings available, and this will be further complicated by the choice of specifi c antibody for a particular experiment. Although antibody cross-reactivity is a serious issue that can further limit any potential "multiplexed" immunological assays, Goldman and colleagues (2004) were still able to demonstrate four-color simultaneous small molecule toxin detection using a commercial plate reader and one excitation wavelength. Although not in vivo diagnostics per se, this did demonstrate the strong potential of QD usage in combination with antibodies.

Nucleic acid detection
From a diagnostic standpoint, QDs have been used in this area primarily as a visualization tool for nucleic acid array detection, in homogenous mutation assays, or as the fl uorophore in fl uorescence in situ hybridization (FISH). Again, this work has been largely "proof of concept" with several different bioconjugation strategies tested. For nucleic acid arrays, the majority of studies have attempted to combine the ability to multiplex with the increased sensitivity derived from QD photostability to improve detection and reduce both the sample and probe concentrations required. Gerion and coworkers demonstrated single nucleotide polymorphism (SNP) of p53 tumor suppressor gene mutations and multiallele detection of hepatitis B and C virus genes with a commercial scanner and two colors of QDs (Gerion et al 2003).
Potential clinical applications of quantum dots array consisting of both QDs and commercial cyanine dyes for co-detection of Bacillus anthracis genes (Shepard 2006). A number of other groups have also used Qbeads or QDs embedded in microspheres to simplify the sample handling, expand the number of available colors by using codes consisting of QD mixtures in each bead, and increase the relative number of QDs hybridized per spot and hence the signal. Xu and colleagues (2003) focused on using such Qbeads for SNP typing while Eastman and colleagues (2006) demonstrated improved sensitivity in gene expression arrays. Homogenous mutation assays based on two-color fl uorescence coincidence analysis have also been reported Yeh et al 2006;Zhang and Johnson 2006). This method labels the target and the probe with different QDs, or QD-dye combinations and then detects the presence of both by their coincidence in a small focal volume. FRET confi gurations of QDs and dyes have also been used in this type of analysis . The principal benefi t of this format is that it can have single molecule sensitivity obviating the need for sample or signal amplifi cation. In QD-based FISH analysis, the focus has been to show that QDs could be superior to conventional fl uorophores due to increased photostability and the ability to multiplex or both. Xiao and Barker demonstrated increased photostability of QD based probes versus conventional dyes for the probing of human metaphase chromosomes (see Figure 5). Chan and colleagues, and Bentolila and Weiss also demonstrated increased sensitivity and the ability to multiplex in these assay formats (Chan et al 2005;Bentolila and Weiss 2006). These results indicate that the relatively large QD-DNA probe complexes can still penetrate the tight cellular and chromosomal structures while retaining hybridization specifi city. In contrast to the bioconjugation issues mentioned above for immunolabeling, the use of streptavidin QDs coupled to biotinylated DNA appears to suffi ce for this type of analysis. However, several groups have commented on the need for long linkers between the DNA probe portion and the QD (Bakalova et al 2005;Chan et al 2005). It is not unreasonable to expect more studies using FISH analysis with QDs in the near future as researchers become increasingly interested in multiplexing simplifi ed optical detection schemes.

Toxicity
The issue of QD toxicity presents perhaps the most serious obstacle to a full exploration of their in vivo usage in biomedical imaging. The toxicity arises primarily from two sources: 1. the semiconductor materials that commonly constitute the QD core (and sometimes the overcoating shell) which can leach under certain circumstances (Derfus et al 2004b), and 2. the generation of reactive and free radical species during excitation (Bakalova et al 2004a(Bakalova et al , 2004b. The toxicity of metals such as Cd and Se is well documented (Colvin 2003;Hoet et al 2004;Hardman 2006;Nel et al 2006). These heavy metals can cross the blood-brain barrier, can accumulate in adipose tissue with biological excretion half-lives greater than ten years, are primarily toxic to the liver and kidneys, and are considered possible teratogens and probable carcinogens (Colvin 2003;Hoet et al 2004;Hardman 2006;Nel et al 2006). In addition to this direct toxicity source, the unique QD nanoscale structure presents a complex set of physiochemical characteristics that further compounds any simple studies or conclusions in this area. As detailed previously, the nanocrystalline cores can be constituted from different combinations of binary semiconductors such as CdSe, CdTe, CdS, and InP. Further, the cores are commonly encapsulated with a secondary semiconductor material and are then functionalized with a variety of surface coating ligands including small thiolated molecules or larger amphiphilic polymers for aqueous compatibility (Medintz et al 2005b;Michalet et al 2005). Additionally, even though the physical dimensions are on the nanoscale, the QDs can have a wide range of sizes with diameters ranging from Ͻ2 nm to greater than 10 nm; the latter is beyond the size limit that is passively excreted by the kidneys (Hoet et al 2004;Michalet et al 2005;Hardman 2006;Nel et al 2006). For biological use, QDs can be further modifi ed with either proteins, such as NeutrAvidin, or other biomolecules such as DNA. Cumulatively, this combination of materials and physical properties serves to confound any systematic study of toxicity, even before issues such as dosage or exposure time are formally addressed. Nevertheless, a body of literature is slowly accumulating that is contributing to our understanding of this important issue.
Although much of the early research in this area was collected piecemeal, a number of excellent review articles have been published that summarize much of what is known in this area. Hoet and colleagues (2004) reviewed most of the data available up until 2004 on the health effects of nanoparticles. The authors point out that upon casual exposure, nanoparticles would enter the body mainly via the lungs and intestines and to a lesser extent the skin. These routes are primarily attributable to the small size of the materials. Hardman (2006) has recently authored one of the most extensive reviews on the toxicity of semiconductor QDs in which the cumulative results from almost all previous in vivo studies (both cellular and small animal) are summarized. This study pays particular attention to the effects of different core-shell materials and their solubilizing ligands. The results that are summarized in these reviews refl ect publications that are primarily observational where the authors have reported the effects of a given QD material on a particular cell line or animal, at some specifi ed concentration(s) for some exposure time (Hoet et al 2004;Hardman 2006). In general, the results were mixed with some reporting no visible toxicity (Jaiswal et al 2003) while others reported high cytotoxicity (Hoshino et al 2004). This can be interpreted to refl ect what is posited above: the choice of core-shell material and solubilizing cap in conjunction with dosage/exposure time will obscure any simple assessments of toxicity. It is also notable that most of the cell lines used in these studies were transformed (immortal and primarily derived from different cancers) and thus might not refl ect either true toxicity or even a normal cellular response.
Recently, several reports have appeared that have focused exclusively on QD toxicity with more systematic approaches. Chan's group has reported on the distribution, sequestration, and clearance of mercaptoundecanoic acid (MUA) functionalized QDs in rats (Fischer et al 2006). They found that QDs coated with bovine serum albumin (BSA) are cleared more rapidly from serum than those coated with crosslinked lysine. Further, most of the BSA coated QDs (99%) are found in the liver after 90 minutes as compared to the lysine QDs (40%). This result refl ects, perhaps, one of the primary metabolic roles of endogenous serum albumin. Interestingly, these QDs were not excreted from the rats even after 10 days. In another study, Chen's group undertook a global molecular and cellular response investigation of skin and lung fi broblast cell lines when exposed to QDs coated with crosslinked silane and polyethylene glycol (PEG) . The authors found negligible toxicity in these model cells and attribute this result to appropriate sequestration of the core-shell material and the biocompatibility of the surface-bound PEG. Mattoussi's group has reported on the dosage effects of QDs on different cell lines (Delehanty et al 2006). They found that acute (1 hour) versus chronic (continuous) exposure and labeling of different concentrations of QDs to two cell lines resulted in a signifi cantly different impact on subsequent cellular viability. No toxicity was noted for acute exposure at all concentrations while chronic exposure manifested signifi cant toxicity and loss of viability with increasing concentration of materials.
More generally, it is important to reiterate that almost all synthetic precursor materials used to make semiconductor QDs are toxic. Some can be physically dangerous since they are pyrophoric, while others are only toxic upon ingestion or prolonged exposure (Medintz et al 2005b). Hardman provides some fascinating perspective on synthetic scales and dosage issues by pointing out that a 2 g synthesis of 100 nm diameter particles contains enough material to dose every human on earth with around 300,000 particles (Hardman 2006). This highlights the importance of proper laboratory procedures during synthesis and appropriate disposal practices as more QD materials are synthesized worldwide. Researchers interested in determining the toxicity of a particular QD (or other nanoparticle) are not without resources. Foreseeing a need in this area, the National Cancer Institute (NCI) in conjunction with several other US health agencies have established the Nanotechnology Characterization Laboratory (NCL) to perform preclinical effi cacy and toxicity testing of nanoparticles (http://ncl.cancer.gov/). As part of its services, the NCL will characterize nanoparticles' physical attributes, their in vitro biological properties, and their in vivo compatibility. The time required to characterize a particular nanomaterial is anticipated to be around 18 months. The Center for Biological and Environmental Nanotechnology (CBEN), a National Science Foundation (NSF) funded Nanoscale Science and Engineering Center at Rice University, provides information and focuses research on the environmental impact and toxicity of a variety of nanomaterials including QDs (http://cohesion.rice.edu/centersandinst/cben/index. cfm). The International Council on Nanotechnology (ICON), CBEN's multi-stakeholder initiative, provides another valuable resource by maintaining a nanomaterial-specifi c Environmental, Health, and Safety Database that can be accessed by the public (http://icon.rice.edu/research.cfm). The continuing debate in this area mirrors that of the general debate on the long-term environmental and health effects of all nanoscale materials (Colvin 2003;Nel et al 2006).

Bimodal luminescent-magnetic quantum dots for imaging
As noted throughout, QDs are excellent deep tissue and in vivo imaging/contrast agents due to several key properties. Although this fi eld is still in its infancy, an application based on combining QD fl uorescent imaging with magnetic resonance imaging (MRI) to deliver superior contrast and possibly detailed three-dimensional in vivo images has already emerged. The bulk of the research in this area has focused on synthesis and characterization. A variety of strategies exist for creating such bimodal materials; these are highlighted in Figure 4. In a one example, Gu and colleagues were able to generate a nanocrystalline luminescent/magnetic CdS/FePt heterodimer in a stepwise manner (Figure 6a; Gu et al 2004). Klimov's group took a slightly different approach by growing a CdSe shell over a Co core (Figure 6b; Kim et al 2005). Rather than using a direct synthesis route, Mulder and colleagues (2006) coated CdSe-ZnS core-shell QDs with a PEGylated phospholipid and a gadolinium diethylene triamine pentaacetate-bisstearylamide (Gd-DTPA-BSA) paramagnetic lipid (Figure 6c). Also bypassing a direct synthesis, Tan and Zhang (2005) encapsulated CdSe-ZnS core-shell QDs and paramagnetic Gd-DTPA chelates into chitosan nanobeads (Figure 6d). Most of these materials have only undergone initial testing to demonstrate that they still retain the parent materials' native luminescent and magnetic properties as synthesized and after intracellular delivery. Some remaining issues before wide-spread implementation include low QD quantum yield and appropriate relaxivity. Despite this, we can expect the great interest in this area to drive the synthesis of a variety of related materials and will shortly begin to see reports of increasingly more detailed in vivo images that have been generated with these bimodal materials. Of particular interest will be the ability to combine these bimodal probes with tumor specifi c antibodies for in vivo delivery and targeting.

Future outlook
What can we expect for the future clinical applications of quantum dots? QDs are already having an impact in molecular pathology. Ventana Medical Systems (http://www.ventanadiscovery.com) has recently started marketing the QD Map family of products. These are immunohistochemistry reagent kits for automated slide processing and fl uorescent detection of fi xed specimens. The selling point of this product family mirrors the list of unique QD photophysical properties, namely photostability, single source excitation, narrow emission, multiplexing capabilities and high quantum yield. However, the lack of consistent and reproducible methods to conjugate many different biomolecules such as antibodies, protein markers, DNA and RNA to QDs in a systematic manner with control over their ratio, orientation, and avidity continues to hamper their further use in clinical diagnostics. As this ability to couple biological recognition agents to QDs slowly improves, we can expect more commercial products incorporating QDs for clinical, diagnostic, and research purposes. The ability to combine QD-based analysis into a multiplexed format, be it immunohistochemistry or in situ nucleic acid hybridization, will be particularly signifi cant and should dramatically increase throughput and engender deep multi-marker studies that analyze correlations between RNA-protein expression and disease state. For biomedical imaging, the unanswered questions and complexity of the QD toxicity issue means that they will almost exclusively be used for research purposes in cellular and animal models.